The advent of high-resolution T1-weighted magnetic resonance (MR) neuroimaging technologies facilitates a detailed exploration into human brain anatomy. Quantitative studies have demonstrated that morphometric and functional responses of cortical and subcortical brain structures are highly correlated to numerous neuropsychiatric illnesses. There now exists a research community supported by universally deployed software packages [1-5] applying automated methods for reconstruction of the human brain structures, which often rely on pre-defined brain atlases. These atlases represent structural and functional information of the brain associated to single-subject, population-averaged, or multiple brain atlas coordinate systems including whole brain based coordinate systems [6-11], white matter based coordinate systems [12-17], and surface based coordinate systems [18-21]; see [22] for an excellent review. Often these are coupled with global deformable template methods, small and large deformation in nature [23-38], for transferring information across anatomical coordinate systems.
In these deformable template approaches, the solutions inherit the smoothness and the topological properties from the atlas. A problem focused on in this disclosure is to extend the generative random diffeomorphic orbit model that has been used in single atlas approaches [30,31,35,39] to the multiple atlas model, in which not only are the diffeomorphic changes in coordinates unknown but also jointly measurable parameters are unknown such as those arising in: (1) atlas labeling corresponding to disease inference, (2) structure parameters such as volumes, or (3) dense label field estimation associated with segmenting the target image into anatomically defined regions. In all the three examples, the atlas in the collection is unknown in generating the image, implying the posterior distribution is multi-modal determined by the multiple atlases. In these global deformable template methods [40], the parameters to be estimated are not “isolated” from the simultaneous acquisition of the global shape phenotype, which is encoded via the structure of the template and the associated deformation.
Since the atlases used for interpreting the image are not known, the conditional-mean technology of the expectation-maximization (EM) algorithm [41] underlies the problem.
Thus there remains a need for improved automated segmentation systems, methods and devices.